On fault probabilities and yield models for analog VLSI neural networks

Investigates the estimation of fault probabilities and yield for analog VLSI implementations of neural computation. The analysis is limited to structures that can be mapped directly onto silicon as truly distributed parallel processing systems. The work improves on the framework suggested recently by Feltham and Maly (1991) and is also applicable to analog or mixed analog/digital VLSI systems.<
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